NNS: Nonlinear Nonparametric Statistics

NNS (Nonlinear Nonparametric Statistics) leverages partial moments – the fundamental elements of variance that asymptotically approximate the area under f(x) – to provide a robust foundation for nonlinear analysis while maintaining linear equivalences. Designed for real-world data that violates symmetry, linearity, or distributional assumptions, NNS delivers a comprehensive suite of advanced statistical techniques, including: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization, Stochastic superiority / dominance and Advanced Monte Carlo sampling. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995, Second edition: <https://ovvo-financial.github.io/NNS/book/>).

Version:	13.0
Depends:	R (≥ 3.6.0)
Imports:	Rcpp, RcppParallel
LinkingTo:	Rcpp, RcppParallel
Suggests:	knitr, rgl, rmarkdown, testthat (≥ 3.0.0)
Published:	2026-06-30
DOI:	10.32614/CRAN.package.NNS
Author:	Fred Viole [aut, cre], Roberto Spadim [ctb], Rasheed Khoshnaw [ctb]
Maintainer:	Fred Viole <ovvo.open.source at gmail.com>
BugReports:	https://github.com/OVVO-Financial/NNS/issues
License:	GPL-3
URL:	https://github.com/OVVO-Financial/NNS
NeedsCompilation:	yes
SystemRequirements:	GNU make
Materials:	README
In views:	Econometrics
CRAN checks:	NNS results

Documentation:

Reference manual:

NNS.html , NNS.pdf

Vignettes:

01. Getting Started with NNS: Overview (source, R code) 02. Getting Started with NNS: Partial Moments (source, R code) 03. Getting Started with NNS: Correlation and Dependence (source, R code) 04. Getting Started with NNS: Normalization and Rescaling (source, R code) 05. Getting Started with NNS: Sampling and Simulation (source, R code) 06. Getting Started with NNS: Comparing Distributions (source, R code) 07. Getting Started with NNS: Clustering and Regression (source, R code) 08. Getting Started with NNS: Classification (source, R code) 09. Getting Started with NNS: Forecasting (source, R code)

Downloads:

Package source:	NNS_13.0.tar.gz
Windows binaries:	r-devel: NNS_13.0.zip, r-release: NNS_12.1.zip, r-oldrel: NNS_13.0.zip
macOS binaries:	r-release (arm64): NNS_13.0.tgz, r-oldrel (arm64): NNS_13.0.tgz, r-release (x86_64): NNS_13.0.tgz, r-oldrel (x86_64): NNS_13.0.tgz
Old sources:	NNS archive

Reverse dependencies:

Reverse imports:	practicalSigni, RandomWalker
Reverse suggests:	influential

Linking:

Please use the canonical form https://CRAN.R-project.org/package=NNS to link to this page.